Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …

Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart
city and Internet of Things (IoT) data. MCS requires large number of users to enable access …

Incentive plays an important role in knowledge discovery, as it impels users to provide high-
quality knowledge. To promise incentive schemes with transparency, blockchain technology …

In current deep learning paradigms, local training or the Standalone framework tends to
result in overfitting and thus low utility. This problem can be addressed by Distributed or …

In federated learning (FL), data owners" share" their local data in a privacy preserving
manner in order to build a federated model, which in turn, can be used to generate revenues …

The current standalone deep learning framework tends to result in overfitting and low utility.
This problem can be addressed by either a centralized framework that deploys a central …

Federated learning is an emerging paradigm to conduct the machine learning
collaboratively but avoid the leakage of original data. Then, how to motivate the data owners …

Providing appropriate monetary rewards is an efficient way for mobile crowdsensing to
motivate the participation of task participants. However, a monetary incentive mechanism is …

As an emerging sensing data collection paradigm, mobile crowdsensing (MCS) enjoys good
scalability and low deployment cost but raises privacy concerns. In this paper, we propose a …

In federated learning (FL), a federation distributedly trains a collective machine learning
model by leveraging privacy preserving technologies. However, FL participants need to …